The widespread use of the Internet provides an enormous amount of information to Internet users. This information includes a multitude of items available for online viewing and/or purchase such as products, services, documents, etc. However, the number of available items have become so large in number that a user can only view a small percentage of available items and will not be able to view many items in which the user may have interest.
Currently, collaborative filtering methods have been developed for Internet application to predict (filter) items that a user may have interest. Collaborative filtering has been used to create recommendation systems that predict individual items (e.g., music CDs, DVD movies, news articles, etc.) that a particular user may have interest, using preference information gathered from a group of other users (collaborating) and upon receiving information about the particular user. These predicted items (recommended items) are then presented to the particular user for viewing and/or purchase. As such, a recommendation system using collaborative filtering produces recommended items that are specific to a particular user, but use information gathered from many other users.
Current recommendation systems based on collaborative filtering, however, do not consider all available online information in producing recommended items. As such, there is a need for a more comprehensive recommendation system using collaborative filtering.